{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload//Experiment3Pt1Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}12 Jan 2025, 09:31:25
{txt}
{com}. 
. use "${c -(}MyProject{c )-}CCES2022processedUPLOAD.dta", clear
{txt}
{com}. 
. * Hegemony
. 
. clonevar hegemony_concern = UMA315
{txt}
{com}. lab var hegemony_concern "US Strengthening or Weakening"
{txt}
{com}. 
. * News interest
. 
. clonevar newsint_num = newsint
{txt}
{com}. recode newsint_num (4 = 3) (7 = .)
{txt}(116 changes made to {bf:newsint_num})

{com}. lab def NEWSINT 3 "Now and then / hardly at all", modify
{txt}
{com}. 
. 
. ********************************************************************************
. * Balance Table
. ********************************************************************************
. 
. recode hispanic (2 = 0)
{txt}(870 changes made to {bf:hispanic})

{com}. 
. lab def HISPANIC 0 "Not Hispanic" 1 "Hispanic", modify
{txt}
{com}.         
. ********************
. * Appendix Table A10
. 
. dtable age female pid3 college white hispanic                                                                   ///
>         , by(subs_treatment) nformat(%8.2g)                                                                                     ///
>         export("${c -(}MyProject{c )-}AppendixTableA10.tex", replace tableonly) ///
>         title("Hegemony Experiment \label{c -(}tab:substitutabilityBalance{c )-}")
{res}
{smcl}
{reset}{...}
{p}Hegemony Experiment \label{tab:substitutabilityBalance}{p_end}
{hline 18}{c -}{hline 12}{c -}{hline 10}{c -}{hline 9}{c -}{hline 10}
{space 18} {space 9}Substitutability Treatment{space 9}
{space 18} Humanitarian Leadership Economics {space 3}Total{space 2}
{hline 18}{c -}{hline 12}{c -}{hline 10}{c -}{hline 9}{c -}{hline 10}
N{space 17} {space 3}{result:282 (34%)} {space 1}{result:277 (33%)} {result:271 (33%)} {result:830 (100%)}
Age (in years){space 4} {space 5}{result:53 (16)} {space 3}{result:54 (17)} {space 2}{result:52 (17)} {space 3}{result:53 (16)}
Female ID{space 9} {space 4}{result:.6 (.49)} {space 2}{result:.53 (.5)} {space 1}{result:.54 (.5)} {space 2}{result:.56 (.5)}
3 point party ID{space 2} {space 5}{result:2 (1.1)} {space 1}{result:2.1 (.98)} {result:2.1 (1.1)} {space 1}{result:2.1 (1.1)}
College Degree{space 4} {space 3}{result:.39 (.49)} {space 1}{result:.41 (.49)} {result:.38 (.49)} {space 1}{result:.39 (.49)}
White Non-Hispanic {space 3}{result:.74 (.44)} {space 1}{result:.76 (.43)} {result:.74 (.44)} {space 1}{result:.75 (.43)}
Hispanic{space 10} {space 2}{result:.073 (.26)} {result:.074 (.26)} {result:.099 (.3)} {result:.082 (.27)}
{hline 18}{c -}{hline 12}{c -}{hline 10}{c -}{hline 9}{c -}{hline 10}
{res}{txt}{p 0 1 2}
(collection {res:DTable} exported to file {browse "/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA10.tex":~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA10.tex})
{p_end}

{com}.         
.         
. 
. ********************************************************************************
. * Descriptive Plot
. ********************************************************************************
. 
. /// Figure 5 A)
> 
. * We begin by creating a catplot showing support for each strategy. This is a
. * slightly more involved process than usual.
. 
. lab def strat3waylab 0 "Disapprove" 1 "Neither" 2 "Approve"
{txt}
{com}. 
. * have to shorten the names because of Stata stupidity
. 
. local strategies contribute concessionbefore coup concessionafter sanctions
{txt}
{com}. 
. * loop to recode the five-way coding to a three-way coding for easier plotting
. 
. foreach var of local strategies {c -(}
{txt}  2{com}.                 clonevar subs_`var'_3way = subs_`var' 
{txt}  3{com}.                 recode subs_`var'_3way (1=0)(2=0)(3=1)(4=2)(5=2)
{txt}  4{com}.                 lab val subs_`var'_3way strat3waylab
{txt}  5{com}. {c )-}
{txt}(170 missing values generated)
(830 changes made to {bf:subs_contribute_3way})
(170 missing values generated)
(830 changes made to {bf:subs_concessionbefore_3way})
(170 missing values generated)
(830 changes made to {bf:subs_coup_3way})
(170 missing values generated)
(830 changes made to {bf:subs_concessionafter_3way})
(170 missing values generated)
(830 changes made to {bf:subs_sanctions_3way})

{com}. 
. catplot                                                                                                                                                 ///
>         , over(subs_contribute_3way)                                                                                            ///
>         over(subs_treatment)                                                                                                            ///
>         asyvars stack percent(subs_treatment)                                                                           ///
>         ylabel(none, nolabels nogrid)   ytitle("")                                                                      ///
>         legend(ring(2) pos(12) rows(1) size(tiny))                                                                      ///
>         blabel(bar,format(%4.1f) box margin(".5 .5 .5 .5")                                                      ///
>                 fcolor(white) color(black) size(3) position(center))                                    ///
>         l1title("")  title("{c -(}it:Contribute{c )-}")                                                                           ///
>         name(gph_contribute_catplot,replace)
{res}{txt}
{com}.         
. catplot                                                                                                                                                 ///
>         , over(subs_concessionbefore_3way)                                                                                      ///
>         over(subs_treatment)                                                                                                            ///
>         asyvars stack percent(subs_treatment)                                                                           ///
>         ylabel(none, nolabels nogrid)   ytitle("")                                                                      ///
>         legend(ring(2) pos(12) rows(1) size(tiny))                                                                      ///
>         blabel(bar,format(%4.1f) box margin(".5 .5 .5 .5")                                                      ///
>                 fcolor(white) color(black) size(3) position(center))                                    ///
>         l1title("")  title("{c -(}it:Concession Before{c )-}")                                                            ///
>         name(gph_concessionbefore_catplot,replace)
{res}{txt}
{com}.                                         
. catplot                                                                                                                                                 ///
>         , over(subs_coup_3way)                                                                                                          ///
>         over(subs_treatment)                                                                                                            ///
>         asyvars stack percent(subs_treatment)                                                                           ///
>         ylabel(none, nolabels nogrid)   ytitle("")                                                                      ///
>         legend(ring(2) pos(12) rows(1) size(tiny))                                                                      ///
>         blabel(bar,format(%4.1f) box margin(".5 .5 .5 .5")                                                      ///
>                 fcolor(white) color(black) size(3) position(center))                                    ///
>         l1title("")  title("{c -(}it:Encourage a Coup{c )-}")                                                                     ///
>         name(gph_coup_catplot,replace)
{res}{txt}
{com}. 
. catplot                                                                                                                                                 ///
>         , over(subs_concessionafter_3way)                                                                                       ///
>         over(subs_treatment)                                                                                                            ///
>         asyvars stack percent(subs_treatment)                                                                           ///
>         ylabel(none, nolabels nogrid)   ytitle("")                                                                      ///
>         legend(ring(2) pos(12) rows(1) size(tiny))                                                                      ///
>         blabel(bar,format(%4.1f) box margin(".5 .5 .5 .5")                                                      ///
>                 fcolor(white) color(black) size(3) position(center))                                    ///
>         l1title("")  title("{c -(}it:Concession After{c )-}")                                                                     ///
>         name(gph_concessionafter_catplot,replace)
{res}{txt}
{com}. 
. catplot                                                                                                                                                 ///
>         , over(subs_sanctions_3way)                                                                                                     ///
>         over(subs_treatment)                                                                                                            ///
>         asyvars stack percent(subs_treatment)                                                                           ///
>         ylabel(none, nolabels nogrid)   ytitle("")                                                                      ///
>         legend(ring(2) pos(12) rows(1) size(tiny))                                                                      ///
>         blabel(bar,format(%4.1f) box margin(".5 .5 .5 .5")                                                      ///
>                 fcolor(white) color(black) size(3) position(center))                                    ///
>         l1title("")  title("{c -(}it:Sanctions{c )-}")                                                                            ///
>         name(gph_sanctions_catplot,replace)
{res}{txt}
{com}. 
. ***** combine everything and come up with one plot, named "gph_catplot"
.                                         
. grc1leg gph_contribute_catplot gph_concessionbefore_catplot gph_coup_catplot    ///
>                 gph_concessionafter_catplot gph_sanctions_catplot                                               ///
>                 , imargin(0) rows(5)                                                                                                    ///
>                 title("{c -(}bf: A) Evaluations of Alternative Strategies{c )-}",size(small))     ///
>                 caption("{c -(}it: N = 830{c )-}", size(vsmall)) ///
>                 name(gph_catplot, replace)
{res}{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{res}{txt}
{com}.                 
.                 
. ********************************************************************************
. * Analyses of Approval of Strategy
. ********************************************************************************
. 
. rename subs_contribute DV_contribute
{res}{txt}
{com}. rename subs_concessionbefore DV_concessionbefore 
{res}{txt}
{com}. rename subs_coup DV_coup
{res}{txt}
{com}. rename subs_concessionafter DV_concessionafter 
{res}{txt}
{com}. rename subs_sanctions DV_sanctions
{res}{txt}
{com}. 
. keep DV_* subs_treatment white faminc_cat age college female covidexposure      ///
>                 politicalknowledge strongerweaker ///
>                 pid7 pid3 caseid newsint_num
{txt}
{com}. 
. 
. reshape long DV_, i(caseid) j(strategy) string
{txt}(j = concessionafter concessionbefore contribute coup sanctions)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       1,000   {txt}->   {res}5,000       
{txt}Number of variables        {res}          18   {txt}->   {res}15          
{txt}j variable (5 values)                     ->   {res}strategy
{txt}xij variables:
{res}DV_concessionafter DV_concessionbefore ... DV_sanctions{txt}->{res}DV_
{txt}{hline 77}

{com}. 
. 
. * just creating a human-readable version of the treatment variable
. encode strategy, gen(strategy_num)
{txt}
{com}. lab def strategy_numlab 1 "Concede After" 2 "Concede Before" 3 "Contribute" 4 "Coup" 5 "Sanctions"
{txt}
{com}. lab val strategy_num strategy_numlab
{txt}
{com}. 
. ologit DV_  b2.subs_treatment i.strategy_num white age college female i.pid7, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-6377.4093}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-6117.6999}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -6115.649}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-6115.6471}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-6115.6471}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:4,150}
{txt}{col 57}{lalign 13:Wald chi2({res:17})}{col 70} = {res}{ralign 6:600.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-6115.6471}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0410}

{txt}{ralign 81:(Std. err. adjusted for {res:830} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .1828789{col 29}{space 2}  .108038{col 40}{space 1}    1.69{col 49}{space 3}0.091{col 57}{space 4}-.0288717{col 70}{space 3} .3946295
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1862352{col 29}{space 2} .1045258{col 40}{space 1}    1.78{col 49}{space 3}0.075{col 57}{space 4}-.0186316{col 70}{space 3} .3911019
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.5615318{col 29}{space 2} .0514021{col 40}{space 1}  -10.92{col 49}{space 3}0.000{col 57}{space 4} -.662278{col 70}{space 3}-.4607855
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .1762152{col 29}{space 2}  .071468{col 40}{space 1}    2.47{col 49}{space 3}0.014{col 57}{space 4} .0361406{col 70}{space 3} .3162898
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.7737824{col 29}{space 2}   .07222{col 40}{space 1}  -10.71{col 49}{space 3}0.000{col 57}{space 4} -.915331{col 70}{space 3}-.6322337
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} .8847568{col 29}{space 2} .0803137{col 40}{space 1}   11.02{col 49}{space 3}0.000{col 57}{space 4} .7273449{col 70}{space 3} 1.042169
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2} .0120398{col 29}{space 2} .1083068{col 40}{space 1}    0.11{col 49}{space 3}0.911{col 57}{space 4}-.2002377{col 70}{space 3} .2243172
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0095018{col 29}{space 2} .0026104{col 40}{space 1}   -3.64{col 49}{space 3}0.000{col 57}{space 4} -.014618{col 70}{space 3}-.0043855
{txt}{space 8}college {c |}{col 17}{res}{space 2}-.1678026{col 29}{space 2} .0935162{col 40}{space 1}   -1.79{col 49}{space 3}0.073{col 57}{space 4} -.351091{col 70}{space 3} .0154858
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0534473{col 29}{space 2} .0897156{col 40}{space 1}    0.60{col 49}{space 3}0.551{col 57}{space 4} -.122392{col 70}{space 3} .2292867
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2}-.0017233{col 29}{space 2} .1396102{col 40}{space 1}   -0.01{col 49}{space 3}0.990{col 57}{space 4}-.2753543{col 70}{space 3} .2719076
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.1550253{col 29}{space 2} .1442698{col 40}{space 1}   -1.07{col 49}{space 3}0.283{col 57}{space 4}-.4377889{col 70}{space 3} .1277383
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.3959148{col 29}{space 2}  .142757{col 40}{space 1}   -2.77{col 49}{space 3}0.006{col 57}{space 4}-.6757133{col 70}{space 3}-.1161163
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.4547617{col 29}{space 2} .1833603{col 40}{space 1}   -2.48{col 49}{space 3}0.013{col 57}{space 4}-.8141412{col 70}{space 3}-.0953822
{txt}Not very str..  {c |}{col 17}{res}{space 2} -.175672{col 29}{space 2} .1679114{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.5047722{col 70}{space 3} .1534283
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.4530108{col 29}{space 2} .1484169{col 40}{space 1}   -3.05{col 49}{space 3}0.002{col 57}{space 4}-.7439026{col 70}{space 3}-.1621191
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .2000179{col 29}{space 2}  .199108{col 40}{space 1}    1.00{col 49}{space 3}0.315{col 57}{space 4}-.1902266{col 70}{space 3} .5902624
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.946876{col 29}{space 2} .2322428{col 57}{space 4}-2.402064{col 70}{space 3}-1.491688
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-.9992516{col 29}{space 2} .2263602{col 57}{space 4}-1.442909{col 70}{space 3}-.5555937
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .4558902{col 29}{space 2} .2244988{col 57}{space 4} .0158807{col 70}{space 3} .8958997
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 1.874145{col 29}{space 2} .2352288{col 57}{space 4} 1.413105{col 70}{space 3} 2.335185
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_results
{txt}
{com}. local N = e(N)
{txt}
{com}. 
. 
. * running different models
. 
. ologit DV_  b2.subs_treatment i.strategy_num white age college female i.pid7, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-6377.4093}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-6117.6999}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -6115.649}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-6115.6471}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-6115.6471}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:4,150}
{txt}{col 57}{lalign 13:Wald chi2({res:17})}{col 70} = {res}{ralign 6:600.26}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-6115.6471}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0410}

{txt}{ralign 81:(Std. err. adjusted for {res:830} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .1828789{col 29}{space 2}  .108038{col 40}{space 1}    1.69{col 49}{space 3}0.091{col 57}{space 4}-.0288717{col 70}{space 3} .3946295
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1862352{col 29}{space 2} .1045258{col 40}{space 1}    1.78{col 49}{space 3}0.075{col 57}{space 4}-.0186316{col 70}{space 3} .3911019
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.5615318{col 29}{space 2} .0514021{col 40}{space 1}  -10.92{col 49}{space 3}0.000{col 57}{space 4} -.662278{col 70}{space 3}-.4607855
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .1762152{col 29}{space 2}  .071468{col 40}{space 1}    2.47{col 49}{space 3}0.014{col 57}{space 4} .0361406{col 70}{space 3} .3162898
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.7737824{col 29}{space 2}   .07222{col 40}{space 1}  -10.71{col 49}{space 3}0.000{col 57}{space 4} -.915331{col 70}{space 3}-.6322337
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} .8847568{col 29}{space 2} .0803137{col 40}{space 1}   11.02{col 49}{space 3}0.000{col 57}{space 4} .7273449{col 70}{space 3} 1.042169
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2} .0120398{col 29}{space 2} .1083068{col 40}{space 1}    0.11{col 49}{space 3}0.911{col 57}{space 4}-.2002377{col 70}{space 3} .2243172
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0095018{col 29}{space 2} .0026104{col 40}{space 1}   -3.64{col 49}{space 3}0.000{col 57}{space 4} -.014618{col 70}{space 3}-.0043855
{txt}{space 8}college {c |}{col 17}{res}{space 2}-.1678026{col 29}{space 2} .0935162{col 40}{space 1}   -1.79{col 49}{space 3}0.073{col 57}{space 4} -.351091{col 70}{space 3} .0154858
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0534473{col 29}{space 2} .0897156{col 40}{space 1}    0.60{col 49}{space 3}0.551{col 57}{space 4} -.122392{col 70}{space 3} .2292867
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2}-.0017233{col 29}{space 2} .1396102{col 40}{space 1}   -0.01{col 49}{space 3}0.990{col 57}{space 4}-.2753543{col 70}{space 3} .2719076
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.1550253{col 29}{space 2} .1442698{col 40}{space 1}   -1.07{col 49}{space 3}0.283{col 57}{space 4}-.4377889{col 70}{space 3} .1277383
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.3959148{col 29}{space 2}  .142757{col 40}{space 1}   -2.77{col 49}{space 3}0.006{col 57}{space 4}-.6757133{col 70}{space 3}-.1161163
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.4547617{col 29}{space 2} .1833603{col 40}{space 1}   -2.48{col 49}{space 3}0.013{col 57}{space 4}-.8141412{col 70}{space 3}-.0953822
{txt}Not very str..  {c |}{col 17}{res}{space 2} -.175672{col 29}{space 2} .1679114{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.5047722{col 70}{space 3} .1534283
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.4530108{col 29}{space 2} .1484169{col 40}{space 1}   -3.05{col 49}{space 3}0.002{col 57}{space 4}-.7439026{col 70}{space 3}-.1621191
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .2000179{col 29}{space 2}  .199108{col 40}{space 1}    1.00{col 49}{space 3}0.315{col 57}{space 4}-.1902266{col 70}{space 3} .5902624
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.946876{col 29}{space 2} .2322428{col 57}{space 4}-2.402064{col 70}{space 3}-1.491688
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-.9992516{col 29}{space 2} .2263602{col 57}{space 4}-1.442909{col 70}{space 3}-.5555937
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .4558902{col 29}{space 2} .2244988{col 57}{space 4} .0158807{col 70}{space 3} .8958997
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 1.874145{col 29}{space 2} .2352288{col 57}{space 4} 1.413105{col 70}{space 3} 2.335185
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_results
{txt}
{com}. local N = e(N)
{txt}
{com}.                 
. coefplot        model_results                                                                                                           ///
>         , name(g_coefplot_strat2, replace)      base                                                                    ///
>         title("{c -(}bf: B) Approval of Strategy{c )-}")                                                                          ///
>         subtitle("{c -(}it: Ordinal Logistic Coefficients Shown; N = `N' strategy evaluations{c )-}", size(small))        ///
>         keep(*.subs_treatment *.strategy*)                                                                                      ///
>         headings(1.subs_treatment="{c -(}bf:Objective{c )-}"                                                                      ///
>                         1.strategy_num="{c -(}bf:Strategy{c )-}")                                                                         ///
>         xline(0) note("Standard errors clustered by respondent. Full model includes controls for demographics and party ID.", size(vsmall))     
{res}{txt}
{com}.         
.         
. * generate substantive implications
. est restore model_results
{txt}(results {stata estimates replay model_results:model_results} are active now)

{com}. margins, atmeans at(subs_treatment=1) dydx(strategy_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:4,150}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.strategy_num 3.strategy_num 4.strategy_num 5.strategy_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(DV_==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(DV_==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(DV_==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(DV_==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(DV_==5), predict(pr outcome(5))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 14:subs_treatment} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 14:1.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:white} = {res:{ralign 8:.7481928}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:age} = {res:{ralign 8:53.11566}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:college} = {res:{ralign 8:.3927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:female} = {res:{ralign 8:.5566265}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:1.pid7} = {res:{ralign 8:.273494}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.pid7} = {res:{ralign 8:.1024096}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.pid7} = {res:{ralign 8:.0915663}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.pid7} = {res:{ralign 8:.139759}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.pid7} = {res:{ralign 8:.0927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:6.pid7} = {res:{ralign 8:.0963855}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:7.pid7} = {res:{ralign 8:.1843373}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:8.pid7} = {res:{ralign 8:.0192771}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.strategy_num {col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1045905{col 29}{space 2} .0100956{col 40}{space 1}   10.36{col 49}{space 3}0.000{col 57}{space 4} .0848036{col 70}{space 3} .1243775
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0339944{col 29}{space 2} .0053844{col 40}{space 1}    6.31{col 49}{space 3}0.000{col 57}{space 4} .0234411{col 70}{space 3} .0445477
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0437807{col 29}{space 2} .0063516{col 40}{space 1}   -6.89{col 49}{space 3}0.000{col 57}{space 4}-.0562295{col 70}{space 3}-.0313318
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0617464{col 29}{space 2} .0065629{col 40}{space 1}   -9.41{col 49}{space 3}0.000{col 57}{space 4}-.0746094{col 70}{space 3}-.0488834
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0330579{col 29}{space 2} .0042452{col 40}{space 1}   -7.79{col 49}{space 3}0.000{col 57}{space 4}-.0413782{col 70}{space 3}-.0247375
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0266606{col 29}{space 2} .0108212{col 40}{space 1}   -2.46{col 49}{space 3}0.014{col 57}{space 4}-.0478698{col 70}{space 3}-.0054514
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0144185{col 29}{space 2} .0059512{col 40}{space 1}   -2.42{col 49}{space 3}0.015{col 57}{space 4}-.0260825{col 70}{space 3}-.0027544
{txt}{space 13}3  {c |}{col 17}{res}{space 2} .0052649{col 29}{space 2} .0028749{col 40}{space 1}    1.83{col 49}{space 3}0.067{col 57}{space 4}-.0003698{col 70}{space 3} .0108995
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0217225{col 29}{space 2} .0087999{col 40}{space 1}    2.47{col 49}{space 3}0.014{col 57}{space 4}  .004475{col 70}{space 3} .0389701
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0140916{col 29}{space 2} .0059734{col 40}{space 1}    2.36{col 49}{space 3}0.018{col 57}{space 4}  .002384{col 70}{space 3} .0257992
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}4.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1512727{col 29}{space 2} .0148658{col 40}{space 1}   10.18{col 49}{space 3}0.000{col 57}{space 4} .1221364{col 70}{space 3} .1804091
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0396567{col 29}{space 2} .0069727{col 40}{space 1}    5.69{col 49}{space 3}0.000{col 57}{space 4} .0259905{col 70}{space 3} .0533229
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0680278{col 29}{space 2} .0088154{col 40}{space 1}   -7.72{col 49}{space 3}0.000{col 57}{space 4}-.0853056{col 70}{space 3}  -.05075
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0810701{col 29}{space 2} .0087595{col 40}{space 1}   -9.26{col 49}{space 3}0.000{col 57}{space 4}-.0982385{col 70}{space 3}-.0639018
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0418315{col 29}{space 2} .0051694{col 40}{space 1}   -8.09{col 49}{space 3}0.000{col 57}{space 4}-.0519634{col 70}{space 3}-.0316996
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}5.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.1062148{col 29}{space 2} .0112226{col 40}{space 1}   -9.46{col 49}{space 3}0.000{col 57}{space 4}-.1282106{col 70}{space 3}-.0842189
{txt}{space 13}2  {c |}{col 17}{res}{space 2} -.075367{col 29}{space 2}   .00755{col 40}{space 1}   -9.98{col 49}{space 3}0.000{col 57}{space 4}-.0901646{col 70}{space 3}-.0605694
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0199905{col 29}{space 2} .0106216{col 40}{space 1}   -1.88{col 49}{space 3}0.060{col 57}{space 4}-.0408085{col 70}{space 3} .0008275
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .1068231{col 29}{space 2} .0102865{col 40}{space 1}   10.38{col 49}{space 3}0.000{col 57}{space 4} .0866619{col 70}{space 3} .1269843
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0947492{col 29}{space 2} .0115748{col 40}{space 1}    8.19{col 49}{space 3}0.000{col 57}{space 4} .0720629{col 70}{space 3} .1174355
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto model_humanitarian_dydx
{txt}
{com}. 
. est restore model_results
{txt}(results {stata estimates replay model_results:model_results} are active now)

{com}. margins, atmeans at(subs_treatment=2) dydx(strategy_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:4,150}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.strategy_num 3.strategy_num 4.strategy_num 5.strategy_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(DV_==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(DV_==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(DV_==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(DV_==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(DV_==5), predict(pr outcome(5))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 14:subs_treatment} = {res:{ralign 8:2}}
{lalign 4:}{space 0}{lalign 14:1.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:white} = {res:{ralign 8:.7481928}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:age} = {res:{ralign 8:53.11566}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:college} = {res:{ralign 8:.3927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:female} = {res:{ralign 8:.5566265}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:1.pid7} = {res:{ralign 8:.273494}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.pid7} = {res:{ralign 8:.1024096}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.pid7} = {res:{ralign 8:.0915663}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.pid7} = {res:{ralign 8:.139759}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.pid7} = {res:{ralign 8:.0927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:6.pid7} = {res:{ralign 8:.0963855}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:7.pid7} = {res:{ralign 8:.1843373}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:8.pid7} = {res:{ralign 8:.0192771}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.strategy_num {col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1138006{col 29}{space 2} .0110914{col 40}{space 1}   10.26{col 49}{space 3}0.000{col 57}{space 4} .0920619{col 70}{space 3} .1355393
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0256511{col 29}{space 2} .0052645{col 40}{space 1}    4.87{col 49}{space 3}0.000{col 57}{space 4} .0153328{col 70}{space 3} .0359693
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0543748{col 29}{space 2} .0066566{col 40}{space 1}   -8.17{col 49}{space 3}0.000{col 57}{space 4}-.0674216{col 70}{space 3} -.041328
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0569426{col 29}{space 2} .0060216{col 40}{space 1}   -9.46{col 49}{space 3}0.000{col 57}{space 4}-.0687446{col 70}{space 3}-.0451405
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0281342{col 29}{space 2} .0035279{col 40}{space 1}   -7.97{col 49}{space 3}0.000{col 57}{space 4}-.0350487{col 70}{space 3}-.0212198
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0297467{col 29}{space 2}  .012025{col 40}{space 1}   -2.47{col 49}{space 3}0.013{col 57}{space 4}-.0533153{col 70}{space 3} -.006178
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0129817{col 29}{space 2} .0054474{col 40}{space 1}   -2.38{col 49}{space 3}0.017{col 57}{space 4}-.0236584{col 70}{space 3}-.0023049
{txt}{space 13}3  {c |}{col 17}{res}{space 2}   .00985{col 29}{space 2} .0042422{col 40}{space 1}    2.32{col 49}{space 3}0.020{col 57}{space 4} .0015355{col 70}{space 3} .0181645
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0207895{col 29}{space 2} .0084632{col 40}{space 1}    2.46{col 49}{space 3}0.014{col 57}{space 4}  .004202{col 70}{space 3}  .037377
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0120888{col 29}{space 2} .0051528{col 40}{space 1}    2.35{col 49}{space 3}0.019{col 57}{space 4} .0019894{col 70}{space 3} .0221882
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}4.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1631531{col 29}{space 2} .0158062{col 40}{space 1}   10.32{col 49}{space 3}0.000{col 57}{space 4} .1321735{col 70}{space 3} .1941327
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0271635{col 29}{space 2} .0068596{col 40}{space 1}    3.96{col 49}{space 3}0.000{col 57}{space 4}  .013719{col 70}{space 3}  .040608
{txt}{space 13}3  {c |}{col 17}{res}{space 2} -.080556{col 29}{space 2} .0089939{col 40}{space 1}   -8.96{col 49}{space 3}0.000{col 57}{space 4}-.0981838{col 70}{space 3}-.0629283
{txt}{space 13}4  {c |}{col 17}{res}{space 2} -.074212{col 29}{space 2} .0080621{col 40}{space 1}   -9.21{col 49}{space 3}0.000{col 57}{space 4}-.0900135{col 70}{space 3}-.0584106
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0355485{col 29}{space 2} .0043643{col 40}{space 1}   -8.15{col 49}{space 3}0.000{col 57}{space 4}-.0441024{col 70}{space 3}-.0269947
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}5.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.1203668{col 29}{space 2} .0122025{col 40}{space 1}   -9.86{col 49}{space 3}0.000{col 57}{space 4}-.1442832{col 70}{space 3}-.0964504
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0736156{col 29}{space 2} .0077748{col 40}{space 1}   -9.47{col 49}{space 3}0.000{col 57}{space 4}-.0888541{col 70}{space 3}-.0583772
{txt}{space 13}3  {c |}{col 17}{res}{space 2} .0033724{col 29}{space 2} .0102975{col 40}{space 1}    0.33{col 49}{space 3}0.743{col 57}{space 4}-.0168104{col 70}{space 3} .0235551
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .1081983{col 29}{space 2} .0104455{col 40}{space 1}   10.36{col 49}{space 3}0.000{col 57}{space 4} .0877254{col 70}{space 3} .1286712
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0824118{col 29}{space 2} .0103777{col 40}{space 1}    7.94{col 49}{space 3}0.000{col 57}{space 4} .0620718{col 70}{space 3} .1027517
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto model_leadership_dydx
{txt}
{com}. 
. est restore model_results
{txt}(results {stata estimates replay model_results:model_results} are active now)

{com}. margins, atmeans at(subs_treatment=3) dydx(strategy_num) post
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:4,150}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 12 12 2}{...}
{p2col:dy/dx wrt:}{res:2.strategy_num 3.strategy_num 4.strategy_num 5.strategy_num}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 13 13 2}{...}
{p2col:{txt:1._predict:}}{res:Pr(DV_==1), predict(pr outcome(1))}{p_end}
{p2col:{txt:2._predict:}}{res:Pr(DV_==2), predict(pr outcome(2))}{p_end}
{p2col:{txt:3._predict:}}{res:Pr(DV_==3), predict(pr outcome(3))}{p_end}
{p2col:{txt:4._predict:}}{res:Pr(DV_==4), predict(pr outcome(4))}{p_end}
{p2col:{txt:5._predict:}}{res:Pr(DV_==5), predict(pr outcome(5))}{p_end}
{p2colreset}{...}

{lalign 4:At: }{space 0}{lalign 14:subs_treatment} = {res:{ralign 8:3}}
{lalign 4:}{space 0}{lalign 14:1.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.strategy_num} = {res:{ralign 8:.2}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:white} = {res:{ralign 8:.7481928}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:age} = {res:{ralign 8:53.11566}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:college} = {res:{ralign 8:.3927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:female} = {res:{ralign 8:.5566265}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:1.pid7} = {res:{ralign 8:.273494}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:2.pid7} = {res:{ralign 8:.1024096}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:3.pid7} = {res:{ralign 8:.0915663}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:4.pid7} = {res:{ralign 8:.139759}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:5.pid7} = {res:{ralign 8:.0927711}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:6.pid7} = {res:{ralign 8:.0963855}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:7.pid7} = {res:{ralign 8:.1843373}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 14:8.pid7} = {res:{ralign 8:.0192771}} {txt:(mean)}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1.strategy_num {col 17}{txt}{c |}  (base outcome)
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1044162{col 29}{space 2}  .010377{col 40}{space 1}   10.06{col 49}{space 3}0.000{col 57}{space 4} .0840776{col 70}{space 3} .1247548
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0341317{col 29}{space 2} .0050458{col 40}{space 1}    6.76{col 49}{space 3}0.000{col 57}{space 4} .0242422{col 70}{space 3} .0440212
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0435648{col 29}{space 2} .0067589{col 40}{space 1}   -6.45{col 49}{space 3}0.000{col 57}{space 4} -.056812{col 70}{space 3}-.0303175
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0618285{col 29}{space 2} .0063772{col 40}{space 1}   -9.70{col 49}{space 3}0.000{col 57}{space 4}-.0743276{col 70}{space 3}-.0493293
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0331547{col 29}{space 2} .0041958{col 40}{space 1}   -7.90{col 49}{space 3}0.000{col 57}{space 4}-.0413784{col 70}{space 3} -.024931
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.0266044{col 29}{space 2} .0108156{col 40}{space 1}   -2.46{col 49}{space 3}0.014{col 57}{space 4}-.0478026{col 70}{space 3}-.0054062
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0144389{col 29}{space 2} .0059387{col 40}{space 1}   -2.43{col 49}{space 3}0.015{col 57}{space 4}-.0260786{col 70}{space 3}-.0027992
{txt}{space 13}3  {c |}{col 17}{res}{space 2} .0051773{col 29}{space 2}   .00285{col 40}{space 1}    1.82{col 49}{space 3}0.069{col 57}{space 4}-.0004085{col 70}{space 3} .0107632
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .0217353{col 29}{space 2}  .008795{col 40}{space 1}    2.47{col 49}{space 3}0.013{col 57}{space 4} .0044975{col 70}{space 3} .0389732
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0141306{col 29}{space 2} .0059734{col 40}{space 1}    2.37{col 49}{space 3}0.018{col 57}{space 4} .0024231{col 70}{space 3} .0258382
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}4.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1510442{col 29}{space 2} .0148031{col 40}{space 1}   10.20{col 49}{space 3}0.000{col 57}{space 4} .1220307{col 70}{space 3} .1800578
{txt}{space 13}2  {c |}{col 17}{res}{space 2} .0398677{col 29}{space 2} .0066016{col 40}{space 1}    6.04{col 49}{space 3}0.000{col 57}{space 4} .0269289{col 70}{space 3} .0528066
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0677669{col 29}{space 2} .0089335{col 40}{space 1}   -7.59{col 49}{space 3}0.000{col 57}{space 4}-.0852761{col 70}{space 3}-.0502576
{txt}{space 13}4  {c |}{col 17}{res}{space 2}-.0811899{col 29}{space 2} .0086695{col 40}{space 1}   -9.36{col 49}{space 3}0.000{col 57}{space 4}-.0981819{col 70}{space 3}-.0641979
{txt}{space 13}5  {c |}{col 17}{res}{space 2}-.0419552{col 29}{space 2} .0052407{col 40}{space 1}   -8.01{col 49}{space 3}0.000{col 57}{space 4}-.0522267{col 70}{space 3}-.0316837
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}5.strategy_num  {txt}{c |}
{space 7}_predict {c |}
{space 13}1  {c |}{col 17}{res}{space 2}-.1059628{col 29}{space 2} .0111755{col 40}{space 1}   -9.48{col 49}{space 3}0.000{col 57}{space 4}-.1278663{col 70}{space 3}-.0840593
{txt}{space 13}2  {c |}{col 17}{res}{space 2}-.0753756{col 29}{space 2} .0075252{col 40}{space 1}  -10.02{col 49}{space 3}0.000{col 57}{space 4}-.0901246{col 70}{space 3}-.0606265
{txt}{space 13}3  {c |}{col 17}{res}{space 2}-.0204133{col 29}{space 2} .0099355{col 40}{space 1}   -2.05{col 49}{space 3}0.040{col 57}{space 4}-.0398866{col 70}{space 3}  -.00094
{txt}{space 13}4  {c |}{col 17}{res}{space 2} .1067658{col 29}{space 2} .0104061{col 40}{space 1}   10.26{col 49}{space 3}0.000{col 57}{space 4} .0863701{col 70}{space 3} .1271614
{txt}{space 13}5  {c |}{col 17}{res}{space 2} .0949859{col 29}{space 2}  .011332{col 40}{space 1}    8.38{col 49}{space 3}0.000{col 57}{space 4} .0727756{col 70}{space 3} .1171961
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. est sto model_economic_dydx
{txt}
{com}. 
. * plot the substantive implications
. 
. * humanitarian first
. 
. mylabels -15(5)20, myscale(@/100) local(myla)
{res}{p}               -.15 "-15"                 -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10"                 .15 "15"                  .2 "20" 
{txt}
{smcl}
{com}. coefplot (model_humanitarian_dydx, keep(2.strategy_num:*._predict) label(Concede Before))       ///
>         (model_humanitarian_dydx, keep(3.strategy_num:*._predict) label(Contribute))                    ///
>         (model_humanitarian_dydx, keep(4.strategy_num:*._predict) label(Coup))                                  ///
>         (model_humanitarian_dydx, keep(5.strategy_num:*._predict) label(Sanctions))                             ///
>         , recast(bar) vertical barw(0.15) noci                                                                                                  ///
>         xlabel(1 "Strongly Disapprove" 2 "Disapprove" 3 "Neither" 4 "Approve" 5 "Strongly Approve")     ///
>         title("{c -(}bf:Differences in Predicted Outcomes, Humanitarian Condition{c )-}", size(medsmall) span)            ///
>                 subtitle("{c -(}it:Base is Concede After{c )-}", size(vsmall) span)                                                       ///
>                 ylab(`myla',labsize(vsmall))                                                                                                            ///
>                 name(g_humanitarian_dydx, replace)                                                                                                      ///
>                 ytitle("Difference between base and treatment", size(vsmall))                                           ///
>                 legend(rows(1) ring(2) pos(12)) yline(0)  
{res}{txt}
{com}. 
. * humanitarian version for main figure (different headline)
. 
. mylabels -15(5)20, myscale(@/100) local(myla)
{res}{p}               -.15 "-15"                 -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10"                 .15 "15"                  .2 "20" 
{txt}
{smcl}
{com}. coefplot (model_humanitarian_dydx, keep(2.strategy_num:*._predict) label(Concede Before))       ///
>         (model_humanitarian_dydx, keep(3.strategy_num:*._predict) label(Contribute))                    ///
>         (model_humanitarian_dydx, keep(4.strategy_num:*._predict) label(Coup))                                  ///
>         (model_humanitarian_dydx, keep(5.strategy_num:*._predict) label(Sanctions))                             ///
>         , recast(bar) vertical barw(0.15) noci                                                                                                  ///
>         xlabel(1 "Strongly Disapprove" 2 "Disapprove" 3 "Neither" 4 "Approve" 5 "Strongly Approve")     ///
>         title("{c -(}bf:C) Differences in Predicted Outcomes, Humanitarian Condition{c )-}", size(medsmall) span) ///
>                 subtitle("{c -(}it:Base is Concede After{c )-}", size(vsmall) span)                                                       ///
>                 ylab(`myla',labsize(vsmall))                                                                                                            ///
>                 name(g_humanitarian_dydx_chart, replace)                                                                                        ///
>                 ytitle("Difference between base and treatment", size(vsmall))                                           ///
>                 legend(rows(1) ring(2) pos(12)) yline(0) 
{res}{txt}
{com}.                 
. * then leadership
. mylabels -15(5)20, myscale(@/100) local(myla)
{res}{p}               -.15 "-15"                 -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10"                 .15 "15"                  .2 "20" 
{txt}
{smcl}
{com}. coefplot (model_leadership_dydx, keep(2.strategy_num:*._predict) label(Concede Before))         ///
>         (model_leadership_dydx, keep(3.strategy_num:*._predict) label(Contribute))                              ///
>         (model_leadership_dydx, keep(4.strategy_num:*._predict) label(Coup))                                    ///
>         (model_leadership_dydx, keep(5.strategy_num:*._predict) label(Sanctions))                               ///
>         , recast(bar) vertical barw(0.15) noci                                                                                                  ///
>         xlabel(1 "Strongly Disapprove" 2 "Disapprove" 3 "Neither" 4 "Approve" 5 "Strongly Approve")     ///
>         title("{c -(}bf:Differences in Predicted Outcomes, Leadership Condition{c )-}", size(medsmall) span)      ///
>                 subtitle("{c -(}it:Base is Concede After{c )-}", size(vsmall) span)                                                       ///
>                 ylab(`myla',labsize(vsmall))                                                                                                            ///
>                 name(g_leader_dydx, replace)                                                                                                            ///
>                 ytitle("Difference between base and treatment", size(vsmall))                                           ///
>                 legend(rows(1) ring(2) pos(12)) yline(0)
{res}{txt}
{com}.                 
. * then economic 
. mylabels -15(5)20, myscale(@/100) local(myla)
{res}{p}               -.15 "-15"                 -.1 "-10"                -.05 "-5"                   0 "0"                 .05 "5"                  .1 "10"                 .15 "15"                  .2 "20" 
{txt}
{smcl}
{com}. coefplot (model_economic_dydx, keep(2.strategy_num:*._predict) label(Concede Before))           ///
>         (model_economic_dydx, keep(3.strategy_num:*._predict) label(Contribute))                                ///
>         (model_economic_dydx, keep(4.strategy_num:*._predict) label(Coup))                                              ///
>         (model_economic_dydx, keep(5.strategy_num:*._predict) label(Sanctions))                                 ///
>         , recast(bar) vertical barw(0.15) noci                                                                                                  ///
>         xlabel(1 "Strongly Disapprove" 2 "Disapprove" 3 "Neither" 4 "Approve" 5 "Strongly Approve")     ///
>         title("{c -(}bf:Differences in Predicted Outcomes, Economic Condition{c )-}", size(medsmall) span)        ///
>                 subtitle("{c -(}it:Base is Concede After{c )-}", size(vsmall) span)                                                       ///
>                 ylab(`myla',labsize(vsmall))                                                                                                            ///
>                 name(g_econ_dydx, replace)                                                                                                                      ///
>                 ytitle("Difference between base and treatment", size(vsmall))                                           ///
>                 legend(rows(1) ring(2) pos(12)) yline(0)
{res}{txt}
{com}. 
. ********************
. * Figure 5
. 
. gr combine  g_coefplot_strat2 g_humanitarian_dydx_chart, name(g_temp, replace) rows(2)
{res}{txt}
{com}. gr combine gph_catplot g_temp, rows(1)
{res}{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
0 not found in class
margin,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. 
. graph export "${c -(}MyProject{c )-}00 Main Figure 5.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/00 Main Figure 5.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. ********************
. * Appendix Figure A7
. 
. * all three substantive combined
. gr combine g_humanitarian_dydx g_leader_dydx g_econ_dydx, name(g_combined, replace) rows(3)
{res}{txt}
{com}. 
. gr display, xsize(6) ysize(9)
{res}{txt}
{com}. 
. gr export "${c -(}MyProject{c )-}01 Figure A7.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/01 Figure A7.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. * run additional models for robustness
. 
. ologit DV_  b2.subs_treatment i.strategy_num white age college female if pid7 <= 3, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3037.8076}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -2898.429}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2897.1118}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2897.1105}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-2897.1105}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,940}
{txt}{col 57}{lalign 13:Wald chi2({res:10})}{col 70} = {res}{ralign 6:303.76}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2897.1105}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0463}

{txt}{ralign 81:(Std. err. adjusted for {res:388} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .3158088{col 29}{space 2} .1452329{col 40}{space 1}    2.17{col 49}{space 3}0.030{col 57}{space 4} .0311576{col 70}{space 3} .6004601
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1523437{col 29}{space 2} .1493364{col 40}{space 1}    1.02{col 49}{space 3}0.308{col 57}{space 4}-.1403502{col 70}{space 3} .4450377
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.6327118{col 29}{space 2}  .076528{col 40}{space 1}   -8.27{col 49}{space 3}0.000{col 57}{space 4}-.7827039{col 70}{space 3}-.4827196
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .2798227{col 29}{space 2} .1092109{col 40}{space 1}    2.56{col 49}{space 3}0.010{col 57}{space 4} .0657732{col 70}{space 3} .4938722
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.9845511{col 29}{space 2} .1087931{col 40}{space 1}   -9.05{col 49}{space 3}0.000{col 57}{space 4}-1.197782{col 70}{space 3}-.7713205
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} .9094344{col 29}{space 2} .1178289{col 40}{space 1}    7.72{col 49}{space 3}0.000{col 57}{space 4}  .678494{col 70}{space 3} 1.140375
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2}-.1403671{col 29}{space 2} .1327964{col 40}{space 1}   -1.06{col 49}{space 3}0.291{col 57}{space 4}-.4006433{col 70}{space 3} .1199092
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0071044{col 29}{space 2} .0034814{col 40}{space 1}   -2.04{col 49}{space 3}0.041{col 57}{space 4}-.0139279{col 70}{space 3}-.0002809
{txt}{space 8}college {c |}{col 17}{res}{space 2}-.1844674{col 29}{space 2} .1190713{col 40}{space 1}   -1.55{col 49}{space 3}0.121{col 57}{space 4} -.417843{col 70}{space 3} .0489081
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1405195{col 29}{space 2}  .129234{col 40}{space 1}    1.09{col 49}{space 3}0.277{col 57}{space 4}-.1127745{col 70}{space 3} .3938134
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.921117{col 29}{space 2} .2610623{col 57}{space 4} -2.43279{col 70}{space 3}-1.409444
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-.8024719{col 29}{space 2} .2514212{col 57}{space 4}-1.295248{col 70}{space 3}-.3096954
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .5329838{col 29}{space 2} .2540135{col 57}{space 4} .0351264{col 70}{space 3} 1.030841
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 1.956064{col 29}{space 2} .2679899{col 57}{space 4} 1.430813{col 70}{space 3} 2.481314
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_dem
{txt}
{com}. 
. ologit DV_  b2.subs_treatment i.strategy_num white age college female if pid7 >= 5 & pid7 < 8, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2356.4756}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2272.7204}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2272.1138}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2272.1134}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-2272.1134}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,550}
{txt}{col 57}{lalign 13:Wald chi2({res:10})}{col 70} = {res}{ralign 6:178.28}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2272.1134}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0358}

{txt}{ralign 81:(Std. err. adjusted for {res:310} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2}-.0459884{col 29}{space 2} .1882779{col 40}{space 1}   -0.24{col 49}{space 3}0.807{col 57}{space 4}-.4150064{col 70}{space 3} .3230296
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1094178{col 29}{space 2} .1694601{col 40}{space 1}    0.65{col 49}{space 3}0.518{col 57}{space 4}-.2227178{col 70}{space 3} .4415535
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.4780296{col 29}{space 2} .0805365{col 40}{space 1}   -5.94{col 49}{space 3}0.000{col 57}{space 4}-.6358782{col 70}{space 3} -.320181
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2}  .206484{col 29}{space 2} .1094239{col 40}{space 1}    1.89{col 49}{space 3}0.059{col 57}{space 4} -.007983{col 70}{space 3} .4209509
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.4776936{col 29}{space 2} .1121563{col 40}{space 1}   -4.26{col 49}{space 3}0.000{col 57}{space 4} -.697516{col 70}{space 3}-.2578712
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} 1.110799{col 29}{space 2} .1311083{col 40}{space 1}    8.47{col 49}{space 3}0.000{col 57}{space 4} .8538318{col 70}{space 3} 1.367767
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2}  .360353{col 29}{space 2} .2981084{col 40}{space 1}    1.21{col 49}{space 3}0.227{col 57}{space 4}-.2239286{col 70}{space 3} .9446347
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0098402{col 29}{space 2} .0046594{col 40}{space 1}   -2.11{col 49}{space 3}0.035{col 57}{space 4}-.0189724{col 70}{space 3} -.000708
{txt}{space 8}college {c |}{col 17}{res}{space 2}-.1443103{col 29}{space 2} .1748109{col 40}{space 1}   -0.83{col 49}{space 3}0.409{col 57}{space 4}-.4869335{col 70}{space 3} .1983128
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0483025{col 29}{space 2} .1474813{col 40}{space 1}   -0.33{col 49}{space 3}0.743{col 57}{space 4}-.3373606{col 70}{space 3} .2407555
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2} -1.18773{col 29}{space 2} .4229582{col 57}{space 4}-2.016713{col 70}{space 3}-.3587473
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2} -.328521{col 29}{space 2} .4148727{col 57}{space 4}-1.141657{col 70}{space 3} .4846146
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .9891543{col 29}{space 2}  .410508{col 57}{space 4} .1845734{col 70}{space 3} 1.793735
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 2.491746{col 29}{space 2} .4434161{col 57}{space 4} 1.622666{col 70}{space 3} 3.360825
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_gop
{txt}
{com}. 
. 
. ologit DV_  b2.subs_treatment i.strategy_num white age  female i.pid7 if college == 0, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3794.1601}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3668.5763}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3667.6564}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3667.6557}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-3667.6557}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,520}
{txt}{col 57}{lalign 13:Wald chi2({res:16})}{col 70} = {res}{ralign 6:273.39}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3667.6557}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0333}

{txt}{ralign 81:(Std. err. adjusted for {res:504} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2}  .210982{col 29}{space 2}  .142582{col 40}{space 1}    1.48{col 49}{space 3}0.139{col 57}{space 4}-.0684736{col 70}{space 3} .4904376
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .2142663{col 29}{space 2} .1407264{col 40}{space 1}    1.52{col 49}{space 3}0.128{col 57}{space 4}-.0615523{col 70}{space 3}  .490085
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.4162634{col 29}{space 2} .0661541{col 40}{space 1}   -6.29{col 49}{space 3}0.000{col 57}{space 4}-.5459231{col 70}{space 3}-.2866037
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .2320138{col 29}{space 2} .0887865{col 40}{space 1}    2.61{col 49}{space 3}0.009{col 57}{space 4} .0579955{col 70}{space 3} .4060321
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.5047105{col 29}{space 2} .0934499{col 40}{space 1}   -5.40{col 49}{space 3}0.000{col 57}{space 4} -.687869{col 70}{space 3}-.3215521
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} .7951689{col 29}{space 2} .1044006{col 40}{space 1}    7.62{col 49}{space 3}0.000{col 57}{space 4} .5905475{col 70}{space 3} .9997903
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2}-.1373763{col 29}{space 2}   .14952{col 40}{space 1}   -0.92{col 49}{space 3}0.358{col 57}{space 4}  -.43043{col 70}{space 3} .1556774
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0111337{col 29}{space 2} .0033278{col 40}{space 1}   -3.35{col 49}{space 3}0.001{col 57}{space 4} -.017656{col 70}{space 3}-.0046114
{txt}{space 9}female {c |}{col 17}{res}{space 2} .0385172{col 29}{space 2} .1171629{col 40}{space 1}    0.33{col 49}{space 3}0.742{col 57}{space 4}-.1911178{col 70}{space 3} .2681523
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2} .0450023{col 29}{space 2} .1836471{col 40}{space 1}    0.25{col 49}{space 3}0.806{col 57}{space 4}-.3149393{col 70}{space 3} .4049439
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.0624758{col 29}{space 2} .2075352{col 40}{space 1}   -0.30{col 49}{space 3}0.763{col 57}{space 4}-.4692373{col 70}{space 3} .3442858
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.4275671{col 29}{space 2} .1884755{col 40}{space 1}   -2.27{col 49}{space 3}0.023{col 57}{space 4}-.7969723{col 70}{space 3}-.0581619
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.1299359{col 29}{space 2} .2271345{col 40}{space 1}   -0.57{col 49}{space 3}0.567{col 57}{space 4}-.5751113{col 70}{space 3} .3152394
{txt}Not very str..  {c |}{col 17}{res}{space 2}-.1859727{col 29}{space 2} .2149208{col 40}{space 1}   -0.87{col 49}{space 3}0.387{col 57}{space 4}-.6072096{col 70}{space 3} .2352643
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}  -.47986{col 29}{space 2} .1978233{col 40}{space 1}   -2.43{col 49}{space 3}0.015{col 57}{space 4}-.8675866{col 70}{space 3}-.0921334
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .2693711{col 29}{space 2} .2573459{col 40}{space 1}    1.05{col 49}{space 3}0.295{col 57}{space 4}-.2350177{col 70}{space 3} .7737598
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-2.096051{col 29}{space 2} .2945716{col 57}{space 4}-2.673401{col 70}{space 3}-1.518702
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-1.162986{col 29}{space 2} .2854917{col 57}{space 4}-1.722539{col 70}{space 3}-.6034323
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .4958384{col 29}{space 2} .2839895{col 57}{space 4}-.0607708{col 70}{space 3} 1.052448
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2}  1.93128{col 29}{space 2} .3025961{col 57}{space 4} 1.338203{col 70}{space 3} 2.524358
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_nocol
{txt}
{com}. 
. ologit DV_  b2.subs_treatment i.strategy_num white age  female i.pid7 if college == 1, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-2545.3017}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-2391.2429}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-2389.5454}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-2389.5426}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-2389.5426}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,630}
{txt}{col 57}{lalign 13:Wald chi2({res:16})}{col 70} = {res}{ralign 6:381.95}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-2389.5426}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0612}

{txt}{ralign 81:(Std. err. adjusted for {res:326} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .1952253{col 29}{space 2} .1770414{col 40}{space 1}    1.10{col 49}{space 3}0.270{col 57}{space 4}-.1517695{col 70}{space 3} .5422201
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1663329{col 29}{space 2}  .167362{col 40}{space 1}    0.99{col 49}{space 3}0.320{col 57}{space 4}-.1616905{col 70}{space 3} .4943563
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.7792729{col 29}{space 2} .0824551{col 40}{space 1}   -9.45{col 49}{space 3}0.000{col 57}{space 4} -.940882{col 70}{space 3}-.6176638
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .0989768{col 29}{space 2} .1197065{col 40}{space 1}    0.83{col 49}{space 3}0.408{col 57}{space 4}-.1356435{col 70}{space 3} .3335972
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-1.183982{col 29}{space 2} .1149949{col 40}{space 1}  -10.30{col 49}{space 3}0.000{col 57}{space 4}-1.409368{col 70}{space 3}-.9585959
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} 1.015568{col 29}{space 2} .1276927{col 40}{space 1}    7.95{col 49}{space 3}0.000{col 57}{space 4} .7652954{col 70}{space 3} 1.265842
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2} .2109423{col 29}{space 2} .1681038{col 40}{space 1}    1.25{col 49}{space 3}0.210{col 57}{space 4}-.1185351{col 70}{space 3} .5404197
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0060698{col 29}{space 2} .0043846{col 40}{space 1}   -1.38{col 49}{space 3}0.166{col 57}{space 4}-.0146634{col 70}{space 3} .0025239
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1313356{col 29}{space 2} .1446278{col 40}{space 1}    0.91{col 49}{space 3}0.364{col 57}{space 4}-.1521297{col 70}{space 3} .4148009
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2}-.0697929{col 29}{space 2} .2242475{col 40}{space 1}   -0.31{col 49}{space 3}0.756{col 57}{space 4}-.5093099{col 70}{space 3} .3697241
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.1996742{col 29}{space 2} .1946161{col 40}{space 1}   -1.03{col 49}{space 3}0.305{col 57}{space 4}-.5811147{col 70}{space 3} .1817663
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.3230201{col 29}{space 2} .2423275{col 40}{space 1}   -1.33{col 49}{space 3}0.183{col 57}{space 4}-.7979733{col 70}{space 3}  .151933
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.8613084{col 29}{space 2} .3049305{col 40}{space 1}   -2.82{col 49}{space 3}0.005{col 57}{space 4}-1.458961{col 70}{space 3}-.2636556
{txt}Not very str..  {c |}{col 17}{res}{space 2}-.0627299{col 29}{space 2} .2954455{col 40}{space 1}   -0.21{col 49}{space 3}0.832{col 57}{space 4}-.6417924{col 70}{space 3} .5163326
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.2992077{col 29}{space 2} .2486185{col 40}{space 1}   -1.20{col 49}{space 3}0.229{col 57}{space 4} -.786491{col 70}{space 3} .1880757
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .1545291{col 29}{space 2} .2209591{col 40}{space 1}    0.70{col 49}{space 3}0.484{col 57}{space 4}-.2785427{col 70}{space 3}  .587601
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.462474{col 29}{space 2} .3796427{col 57}{space 4} -2.20656{col 70}{space 3}-.7183879
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-.4575162{col 29}{space 2} .3741291{col 57}{space 4}-1.190796{col 70}{space 3} .2757634
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .7008695{col 29}{space 2} .3710585{col 57}{space 4}-.0263917{col 70}{space 3} 1.428131
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 2.130996{col 29}{space 2} .3854287{col 57}{space 4}  1.37557{col 70}{space 3} 2.886423
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_college
{txt}
{com}. 
. 
. ologit DV_  b2.subs_treatment i.strategy_num white age  female i.pid7 college if newsint_num  == 1, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3360.3002}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3184.9974}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3183.1884}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3183.1864}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-3183.1864}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,165}
{txt}{col 57}{lalign 13:{help j_robustsingular##|_new:Wald chi2(16)}}{col 70} = {res}{ralign 6:.}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:.}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3183.1864}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0527}

{txt}{ralign 81:(Std. err. adjusted for {res:433} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .1462531{col 29}{space 2}  .152857{col 40}{space 1}    0.96{col 49}{space 3}0.339{col 57}{space 4}-.1533412{col 70}{space 3} .4458473
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .1591172{col 29}{space 2} .1487997{col 40}{space 1}    1.07{col 49}{space 3}0.285{col 57}{space 4}-.1325249{col 70}{space 3} .4507594
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.5768894{col 29}{space 2} .0684837{col 40}{space 1}   -8.42{col 49}{space 3}0.000{col 57}{space 4} -.711115{col 70}{space 3}-.4426639
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2}  .264282{col 29}{space 2} .1017134{col 40}{space 1}    2.60{col 49}{space 3}0.009{col 57}{space 4} .0649275{col 70}{space 3} .4636366
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2} -.886671{col 29}{space 2} .1030402{col 40}{space 1}   -8.61{col 49}{space 3}0.000{col 57}{space 4}-1.088626{col 70}{space 3} -.684716
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} 1.051407{col 29}{space 2} .1079898{col 40}{space 1}    9.74{col 49}{space 3}0.000{col 57}{space 4} .8397511{col 70}{space 3} 1.263063
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2} .1347155{col 29}{space 2} .1721781{col 40}{space 1}    0.78{col 49}{space 3}0.434{col 57}{space 4}-.2027474{col 70}{space 3} .4721783
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0034133{col 29}{space 2} .0046176{col 40}{space 1}   -0.74{col 49}{space 3}0.460{col 57}{space 4}-.0124636{col 70}{space 3} .0056371
{txt}{space 9}female {c |}{col 17}{res}{space 2} .1105381{col 29}{space 2} .1246628{col 40}{space 1}    0.89{col 49}{space 3}0.375{col 57}{space 4}-.1337965{col 70}{space 3} .3548726
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2} .3052783{col 29}{space 2} .2395403{col 40}{space 1}    1.27{col 49}{space 3}0.203{col 57}{space 4}-.1642122{col 70}{space 3} .7747687
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.1108919{col 29}{space 2} .1657573{col 40}{space 1}   -0.67{col 49}{space 3}0.503{col 57}{space 4}-.4357702{col 70}{space 3} .2139864
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.6779542{col 29}{space 2} .2589589{col 40}{space 1}   -2.62{col 49}{space 3}0.009{col 57}{space 4}-1.185504{col 70}{space 3}-.1704042
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.2412699{col 29}{space 2} .2039718{col 40}{space 1}   -1.18{col 49}{space 3}0.237{col 57}{space 4}-.6410472{col 70}{space 3} .1585074
{txt}Not very str..  {c |}{col 17}{res}{space 2}-.1662327{col 29}{space 2}  .248543{col 40}{space 1}   -0.67{col 49}{space 3}0.504{col 57}{space 4}-.6533681{col 70}{space 3} .3209027
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.6775254{col 29}{space 2} .1919889{col 40}{space 1}   -3.53{col 49}{space 3}0.000{col 57}{space 4}-1.053817{col 70}{space 3}-.3012341
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} 1.466388{col 29}{space 2}  .273401{col 40}{space 1}    5.36{col 49}{space 3}0.000{col 57}{space 4} .9305322{col 70}{space 3} 2.002244
{txt}{space 15} {c |}
{space 8}college {c |}{col 17}{res}{space 2}-.1309665{col 29}{space 2} .1294733{col 40}{space 1}   -1.01{col 49}{space 3}0.312{col 57}{space 4}-.3847294{col 70}{space 3} .1227965
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-1.202179{col 29}{space 2} .3732015{col 57}{space 4}-1.933641{col 70}{space 3}-.4707175
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-.2948914{col 29}{space 2} .3721363{col 57}{space 4}-1.024265{col 70}{space 3} .4344822
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .6965672{col 29}{space 2} .3751531{col 57}{space 4}-.0387194{col 70}{space 3} 1.431854
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 2.162436{col 29}{space 2} .3914702{col 57}{space 4} 1.395169{col 70}{space 3} 2.929704
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_news1
{txt}
{com}. 
. ologit DV_  b2.subs_treatment i.strategy_num white age  female i.pid7 college if newsint_num  == 2, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1606.1274}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1528.9167}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1527.9871}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1527.9858}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1527.9858}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,065}
{txt}{col 57}{lalign 13:Wald chi2({res:17})}{col 70} = {res}{ralign 6:183.03}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1527.9858}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0487}

{txt}{ralign 81:(Std. err. adjusted for {res:213} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .2837717{col 29}{space 2} .2225871{col 40}{space 1}    1.27{col 49}{space 3}0.202{col 57}{space 4}-.1524909{col 70}{space 3} .7200344
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} .4128484{col 29}{space 2} .2357638{col 40}{space 1}    1.75{col 49}{space 3}0.080{col 57}{space 4}-.0492402{col 70}{space 3}  .874937
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.4730591{col 29}{space 2} .1127552{col 40}{space 1}   -4.20{col 49}{space 3}0.000{col 57}{space 4}-.6940552{col 70}{space 3}-.2520629
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2} .1272809{col 29}{space 2} .1397064{col 40}{space 1}    0.91{col 49}{space 3}0.362{col 57}{space 4}-.1465386{col 70}{space 3} .4011003
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2}-.7405389{col 29}{space 2} .1430514{col 40}{space 1}   -5.18{col 49}{space 3}0.000{col 57}{space 4}-1.020915{col 70}{space 3}-.4601633
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} 1.018968{col 29}{space 2} .1662028{col 40}{space 1}    6.13{col 49}{space 3}0.000{col 57}{space 4} .6932161{col 70}{space 3} 1.344719
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2}-.1419695{col 29}{space 2} .2317507{col 40}{space 1}   -0.61{col 49}{space 3}0.540{col 57}{space 4}-.5961926{col 70}{space 3} .3122535
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0112739{col 29}{space 2} .0053758{col 40}{space 1}   -2.10{col 49}{space 3}0.036{col 57}{space 4}-.0218103{col 70}{space 3}-.0007375
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0716119{col 29}{space 2} .1834712{col 40}{space 1}   -0.39{col 49}{space 3}0.696{col 57}{space 4}-.4312088{col 70}{space 3}  .287985
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2} .0926122{col 29}{space 2} .2410686{col 40}{space 1}    0.38{col 49}{space 3}0.701{col 57}{space 4}-.3798736{col 70}{space 3}  .565098
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.2686648{col 29}{space 2} .3432356{col 40}{space 1}   -0.78{col 49}{space 3}0.434{col 57}{space 4}-.9413942{col 70}{space 3} .4040646
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}  -.48287{col 29}{space 2} .3029391{col 40}{space 1}   -1.59{col 49}{space 3}0.111{col 57}{space 4} -1.07662{col 70}{space 3} .1108797
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-.6459864{col 29}{space 2} .4319094{col 40}{space 1}   -1.50{col 49}{space 3}0.135{col 57}{space 4}-1.492513{col 70}{space 3} .2005404
{txt}Not very str..  {c |}{col 17}{res}{space 2}-.0563956{col 29}{space 2} .3877922{col 40}{space 1}   -0.15{col 49}{space 3}0.884{col 57}{space 4}-.8164544{col 70}{space 3} .7036632
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.1185273{col 29}{space 2}  .266569{col 40}{space 1}   -0.44{col 49}{space 3}0.657{col 57}{space 4}-.6409929{col 70}{space 3} .4039384
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .0894269{col 29}{space 2} .5012689{col 40}{space 1}    0.18{col 49}{space 3}0.858{col 57}{space 4}-.8930421{col 70}{space 3} 1.071896
{txt}{space 15} {c |}
{space 8}college {c |}{col 17}{res}{space 2}-.2183126{col 29}{space 2} .1965051{col 40}{space 1}   -1.11{col 49}{space 3}0.267{col 57}{space 4}-.6034555{col 70}{space 3} .1668303
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-2.448433{col 29}{space 2}  .423697{col 57}{space 4}-3.278864{col 70}{space 3}-1.618002
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-1.329249{col 29}{space 2} .4036495{col 57}{space 4}-2.120387{col 70}{space 3}-.5381103
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .3994751{col 29}{space 2} .4007477{col 57}{space 4} -.385976{col 70}{space 3} 1.184926
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 1.919385{col 29}{space 2} .4203707{col 57}{space 4} 1.095474{col 70}{space 3} 2.743297
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_news2
{txt}
{com}. 
. 
. ologit DV_  b2.subs_treatment i.strategy_num white age  female i.pid7 college if newsint_num  == 3, cluster(caseid)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1162.7177}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1113.8717}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1113.2124}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1113.2117}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1113.2117}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:845}
{txt}{col 57}{lalign 13:Wald chi2({res:17})}{col 70} = {res}{ralign 6:103.09}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1113.2117}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0426}

{txt}{ralign 81:(Std. err. adjusted for {res:169} clusters in {res:caseid})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}            DV_{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}subs_treatment {c |}
{space 2}Humanitarian  {c |}{col 17}{res}{space 2} .0101594{col 29}{space 2} .2403162{col 40}{space 1}    0.04{col 49}{space 3}0.966{col 57}{space 4}-.4608517{col 70}{space 3} .4811705
{txt}{space 5}Economics  {c |}{col 17}{res}{space 2} -.018986{col 29}{space 2} .2414157{col 40}{space 1}   -0.08{col 49}{space 3}0.937{col 57}{space 4} -.492152{col 70}{space 3}   .45418
{txt}{space 15} {c |}
{space 3}strategy_num {c |}
Concede Before  {c |}{col 17}{res}{space 2}-.6760751{col 29}{space 2} .1256508{col 40}{space 1}   -5.38{col 49}{space 3}0.000{col 57}{space 4}-.9223461{col 70}{space 3}-.4298041
{txt}{space 4}Contribute  {c |}{col 17}{res}{space 2}-.0334414{col 29}{space 2} .1575617{col 40}{space 1}   -0.21{col 49}{space 3}0.832{col 57}{space 4}-.3422567{col 70}{space 3}  .275374
{txt}{space 10}Coup  {c |}{col 17}{res}{space 2} -.743502{col 29}{space 2} .1717221{col 40}{space 1}   -4.33{col 49}{space 3}0.000{col 57}{space 4}-1.080071{col 70}{space 3}-.4069328
{txt}{space 5}Sanctions  {c |}{col 17}{res}{space 2} .1884745{col 29}{space 2} .1967456{col 40}{space 1}    0.96{col 49}{space 3}0.338{col 57}{space 4}-.1971399{col 70}{space 3} .5740888
{txt}{space 15} {c |}
{space 10}white {c |}{col 17}{res}{space 2}-.0356265{col 29}{space 2} .2368023{col 40}{space 1}   -0.15{col 49}{space 3}0.880{col 57}{space 4}-.4997505{col 70}{space 3} .4284975
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0200619{col 29}{space 2} .0056584{col 40}{space 1}   -3.55{col 49}{space 3}0.000{col 57}{space 4}-.0311523{col 70}{space 3}-.0089716
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.1666536{col 29}{space 2} .2049674{col 40}{space 1}   -0.81{col 49}{space 3}0.416{col 57}{space 4}-.5683823{col 70}{space 3}  .235075
{txt}{space 15} {c |}
{space 11}pid7 {c |}
Not very str..  {c |}{col 17}{res}{space 2} -.736562{col 29}{space 2} .4441924{col 40}{space 1}   -1.66{col 49}{space 3}0.097{col 57}{space 4}-1.607163{col 70}{space 3} .1340392
{txt}{space 1}Lean Democrat  {c |}{col 17}{res}{space 2}-.1938866{col 29}{space 2} .5185659{col 40}{space 1}   -0.37{col 49}{space 3}0.708{col 57}{space 4}-1.210257{col 70}{space 3} .8224838
{txt}{space 3}Independent  {c |}{col 17}{res}{space 2}-.1037666{col 29}{space 2} .3979547{col 40}{space 1}   -0.26{col 49}{space 3}0.794{col 57}{space 4}-.8837434{col 70}{space 3} .6762103
{txt}Lean Republi..  {c |}{col 17}{res}{space 2}-1.311121{col 29}{space 2} .6216174{col 40}{space 1}   -2.11{col 49}{space 3}0.035{col 57}{space 4}-2.529469{col 70}{space 3}-.0927733
{txt}Not very str..  {c |}{col 17}{res}{space 2} -.414438{col 29}{space 2} .4735051{col 40}{space 1}   -0.88{col 49}{space 3}0.381{col 57}{space 4}-1.342491{col 70}{space 3}  .513615
{txt}Strong Repub..  {c |}{col 17}{res}{space 2}-.2014871{col 29}{space 2} .5322325{col 40}{space 1}   -0.38{col 49}{space 3}0.705{col 57}{space 4}-1.244644{col 70}{space 3} .8416694
{txt}{space 6}Not sure  {c |}{col 17}{res}{space 2} .4038498{col 29}{space 2} .4361983{col 40}{space 1}    0.93{col 49}{space 3}0.355{col 57}{space 4}-.4510831{col 70}{space 3} 1.258783
{txt}{space 15} {c |}
{space 8}college {c |}{col 17}{res}{space 2}-.0565309{col 29}{space 2} .2418475{col 40}{space 1}   -0.23{col 49}{space 3}0.815{col 57}{space 4}-.5305433{col 70}{space 3} .4174814
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}/cut1 {c |}{col 17}{res}{space 2}-3.430652{col 29}{space 2} .5709481{col 57}{space 4} -4.54969{col 70}{space 3}-2.311614
{txt}{space 10}/cut2 {c |}{col 17}{res}{space 2}-2.272649{col 29}{space 2} .5430531{col 57}{space 4}-3.337014{col 70}{space 3}-1.208285
{txt}{space 10}/cut3 {c |}{col 17}{res}{space 2} .0692498{col 29}{space 2} .5371092{col 57}{space 4} -.983465{col 70}{space 3} 1.121965
{txt}{space 10}/cut4 {c |}{col 17}{res}{space 2} 1.460417{col 29}{space 2} .5941533{col 57}{space 4} .2958983{col 70}{space 3} 2.624936
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto model_news3
{txt}
{com}. 
. 
. ********************
. * Appendix Table A11
. 
. esttab model_results model_dem  model_gop model_news1 model_news2 model_news3                   ///
>                 using "${c -(}MyProject{c )-}AppendixTableA11.tex"                ///
>                 , replace nobase label  longtable  noomitted                                                    ///
>                 drop(*cut*)                                     ///
>                 mtitles("Base" "Democrats Only" "GOP Only" "News Mostly" "News Some" "News Rarely")             ///
>                 title("Substitutability Experiment Results" "\label{c -(}tab:substitutabilitydovreq{c )-}")
{res}{txt}{p 0 4 2}
(file {bf}
/Users/rpm47/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA11.tex{rm}
not found)
{p_end}
(output written to {browse  `"~/Dropbox/0001 Academic Projects/Ongoing/0155 Voting Abroad Purpose/Replication/Upload/AppendixTableA11.tex"'})

{com}. 
. log close
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